People apply Bayesian methods in many areas: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a desirable feature for fields like medicine.
When applied to deep learning, Bayesian methods allow you to compress your models a hundred folds, and automatically tune hyperparameters, saving your time and money.
In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it. We will see how one can automate this workflow and how to speed it up using some advanced techniques.
We will also see applications of Bayesian methods to deep learning and how to generate new images with it. We will see how new drugs that cure severe diseases be found with Bayesian methods.

从本节课中

Gaussian processes & Bayesian optimization

Welcome to the final week of our course! This time we will see nonparametric Bayesian methods. Specifically, we will learn about Gaussian processes and their application to Bayesian optimization that allows one to perform optimization for scenarios in which each function evaluation is very expensive: oil probe, drug discovery and neural network architecture tuning.